A Novel Active Contour Model for MRI Brain Segmentation used in Radiotherapy Treatment Planning
Introduction: Brain image segmentation is one of the most important clinical tools used in radiology and radiotherapy. But accurate segmentation is a very difficult task because these images mostly contain noise, inhomogeneities, and sometimes aberrations. The purpose of this study was to introduc...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Electronic Physician
2016-05-01
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| Series: | Electronic Physician |
| Subjects: | |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4930267/ |
| Summary: | Introduction: Brain image segmentation is one of the most important clinical tools used in radiology and
radiotherapy. But accurate segmentation is a very difficult task because these images mostly contain noise,
inhomogeneities, and sometimes aberrations. The purpose of this study was to introduce a novel, locally
statistical active contour model (ACM) for magnetic resonance image segmentation in the presence of intense
inhomogeneity with the ability to determine the position of contour and energy diagram.
Methods: A Gaussian distribution model with different means and variances was used for inhomogeneity, and a
moving window was used to map the original image into another domain in which the intensity distributions of
inhomogeneous objects were still Gaussian but were better separated. The means of the Gaussian distributions in
the transformed domain can be adaptively estimated by multiplying a bias field by the original signal within the
window. Then, a statistical energy function is defined for each local region. Also, to evaluate the performance of
our method, experiments were conducted on MR images of the brain for segment tumors or normal tissue as
visualization and energy functions.
Results: In the proposed method, we were able to determine the size and position of the initial contour and to
count iterations to have a better segmentation. The energy function for 20 to 430 iterations was calculated. The
energy function was reduced by about 5and 7% after 70 and 430 iterations, respectively. These results showed
that, with increasing iterations, the energy function decreased, but it decreased faster during the early iterations,
after which it decreased slowly. Also, this method enables us to stop the segmentation based on the threshold that
we define for the energy equation.
Conclusion: An active contour model based on the energy function is a useful tool for medical image
segmentation. The proposed method combined the information about neighboring pixels that belonged to the
same class, thereby making it strong to separate the desired objects from the background. |
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| ISSN: | 2008-5842 2008-5842 |